Big data analytics are critical in 5G mobile networks to make 5G mobile networks autonomic (e.g. self-scaling, self-configuring, self-healing and self-optimizing). These big data analytics make meeting the end-to-end performance and quality of experience (“QoE”) of 5G mobile network applications and services effective by exporting real-time and predictive insights about the 5G mobile networks to applications and self-adapting networks to meet the needs of the specific applications.
In order to meet the needs of the 5G mobile networks, there is a need for a generic framework for big data analytics. A further requirement is that the framework has to be generic enough to allow for creating a multi-vendor market for analytics module plug-ins.
Without such a generic framework for 5G mobile networks, the issues which would persist include how to provide native analytics capabilities in 5G mobile networks instead of the current solution which is an after-thought of add-on analytics (such as in existing 2G, 3G and 4G mobile networks). Other issues that would persist include how to handle large volumes of data generated in 5G mobile networks and how to handle high velocity of data arrival in 5G mobile networks
Furthermore, other issues that would persist include how to manage the wide variety and heterogeneity of data generated in the 5G mobile networks, how to collect and process data from diverse and geographically distributed data sources in the 5G mobile networks, how to meet ultra-low latency reaction times for many 5G mobile network mission critical applications such as connected autonomous vehicles, or remote robotic control, and how to enable extension of the system to support future data sources and analytics techniques.
Existing mobile networks (e.g. 2G, 3G and 4G mobile networks) were not designed with analytics as a necessary and key requirement in the architecture. In order to add an analytics framework to the existing mobile networks would require extensive network re-work and customization which would result in cost-prohibitive and intrusive changes in the deployed networks. As a result, the existing analytics solutions are at best ad-hoc add-ons, which are susceptible to deficiencies in the networks.
At present, there is no generic network insights service that exposes predictive network information and can serve as a foundation for the 5G mobile networks and application needs. The existing solutions exposing network state information are very narrow and are tied to a customized data source mining.
The best existing solution for 4G LTE mobile networks include analytics add-on devices that monitor and mirror the traffic using network probes (e.g. between a Mobile Management Entity (“MME”)) and a Serving Gateway (“SGW”) and between SGW and Packet Data Network Gateway (“PGW”)) and transferring the mirrored data to the central analytics function (typically located in the data center) for processing.
There are several examples of data analytics in existing mobile networks. For example, FIG. 1 is related art which is directed towards an analytics add-on device for a mobile network 100. The mobile network 100 includes a radio network 107 and a core network 108. The radio network 107 includes mobile devices 106 and an Evolved Node B (“eNodeB”) 109. The core network 108 includes a SGW 102 and a PGW 103. The core network 108 further includes an analytics add-on 101 and a MME 110. The analytics add-on device 101 mirrors and monitors the traffic between the MME 110 and the SGW 102, between the SGW 102 and the PGW 103, and between the PGW 103 and the PCRF 111. The solutions implemented in the mobile network 100 are inefficient and do not meet the listed above needs because the add-on analytics device 101 has significant issues for the following reasons: (a) the add-on analytic device 101 is not able to identify the class of application (email, web browsing, video streams) that the end-to-end encrypted stream represents (recall that end-to-end encryption renders deep packet inspection currently used for application class identification ineffective); (b) significant portions of the data used internally by VNFs is not designed to be exposed to other entities or not designed to be transmitted over the wires from the VNF; (c) the add-on device 101 is unable to provide self-healing in real-time and nor is it able to produce and deliver network insights in real-time.
End-to-end encryption is expected to be prevalent in the 5G mobile networks. Currently, data analysis is being conducted post-hoc (i.e. not in real time) and large data sets need to be transferred over the network to the data center for offline analysis. Furthermore, layer 1 and layer 2 wireless bearer resource allocations and channel conditions information are not available to the analytics add-on device 101. While large amounts of data are available at scheduler, Radio Link Control (“RLC”), Packet Data Convergence Protocol (“PDCP”) and antenna levels, there is no mechanism available to expose this large amount of data to analytics, in real time.
The existing solutions are strongly tied to proprietary methods of exposing and processing vendor-specific types and formats of data and this is not easily extensible to support future data sources and analytics techniques.
Therefore, there is a need for a generic framework for analytics in 5G mobile networks to allow for creating a multi-vendor market for analytics module plug-ins.